mlsanityData to 7 Oct 2026

Understanding Reasoning in LLMs through Strategic Information Allocation under Uncertainty

arXiv 2603.15500 ↗First posted 16 Mar 2026cs.AI cs.LGcited by 8

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

LLMs often exhibit Aha moments such as self-correction after tokens like "Wait," yet the underlying mechanism remains unclear. Standard LLMs collapse mainly through silent divergence, where trajectories drift from the correct answer yet remain locally coherent, so no explicit error triggers reactive self-correction. We introduce an information-theoretic framework that separates reasoning into procedural advancement and epistemic verbalization, the token-level externalization of uncertainty, and prove that sporadic verbalization restores convergence toward the correct answer even without explicit error triggers. Empirically, a minimal doubt cue recovers failed trajectories, and small-scale SFT suffices to instill or suppress this capability, suggesting that strong reasoning hinges less on an extraordinary inner mechanism than on the linguistic habit of externalizing uncertainty. Our framework recasts reasoning as strategic information allocation under uncertainty, offering a new lens for understanding and advancing LLM reasoning.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 37 of its 48 references that are in the map. First posted on arXiv 16 Mar 2026.

Builds on

37 of 48 references.

Closest ancestors · 7

Same direction, newest first

  1. 2026The Illusion of Insight in Reasoning Models2 Jan
  2. 2025Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning3 Jun
  3. 2025Think or Not? Exploring Thinking Efficiency in Large Reasoning Models via an Information-Theoretic Lens23 May
  4. 2025Dynamic Early Exit in Reasoning Models22 Apr
  5. 2025Understanding Aha Moments: from External Observations to Internal Mechanisms3 Apr
  6. 2025s1: Simple test-time scaling31 Jan
  7. 2023Large Language Models Cannot Self-Correct Reasoning Yet3 Oct

Other references · 30

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2025Qwen3 Technical Reportcited by 8,195
  4. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  5. 2025Gemma 3 Technical Reportcited by 1,686
  6. 2024LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Modelscited by 1,442
  7. 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,371
  8. 2024Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvementcited by 1,057
  9. 2025Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoningcited by 540
  10. 2025LIMO: Less is More for Reasoningcited by 488
  11. 2024Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMscited by 265
  12. 2025When More is Less: Understanding Chain-of-Thought Length in LLMscited by 182
18 more
  1. 2025LIMR: Less is More for RL Scalingcited by 136
  2. 2022Learning by Distilling Contextcited by 128
  3. 2025Small Models Struggle to Learn from Strong Reasonerscited by 109
  4. 2025Beyond Binary Rewards: Training LMs to Reason About Their Uncertaintycited by 107
  5. 2025RLVMR: Reinforcement Learning with Verifiable Meta-Reasoning Rewards for Robust Long-Horizon Agentscited by 56
  6. 2024Evaluating LLMs at Detecting Errors in LLM Responsescited by 49
  7. 2025Do Not Let Low-Probability Tokens Over-Dominate in RL for LLMscited by 43
  8. 2024Understanding Chain-of-Thought in LLMs through Information Theorycited by 36
  9. 2025ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Reflectioncited by 30
  10. 2024Step-by-Step Reasoning for Math Problems via Twisted Sequential Monte Carlocited by 21
  11. 2025AgentMath: Empowering Mathematical Reasoning for Large Language Models via Tool-Augmented Agentcited by 17
  12. 2026Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimizationcited by 17
  13. 2025Through the Valley: Path to Effective Long CoT Training for Small Language Modelscited by 10
  14. 2025Semantic Soft Bootstrapping: Long Context Reasoning in LLMs without Reinforcement Learningcited by 9
  15. 2025Why Less is More (Sometimes): A Theory of Data Curationcited by 8
  16. 2025Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theorycited by 4
  17. 2025Why Does Reasoning Length Converge? Unveiling the Underfitting-Overfitting Trade-off in Chain-of-Thoughtcited by 4
  18. 2025RAISE: Enhancing Scientific Reasoning in LLMs via Step-by-Step Retrievalcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 8.

8 papers.

  1. 2026Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs?cited by 87
  2. 2026ReNIO: Reweighting Negative Trajectory Importance for LLM On-Policy Distillationcited by 5
  3. 2026Agent Explorative Policy Optimization for Multimodal Agentic Reasoningcited by 2
  4. 2026Process Supervision of Confidence Margin for Calibrated LLM Reasoningcited by 1
  5. 2026Distilling LLM Feedback for Lean Theorem Provingcited by 1
  6. 2026Exploring Extrinsic and Intrinsic Properties for Effective Reasoning with Code Interpretercited by 0
  7. 2026Surprising Success, Repeated Failure: Entropy-Guided Credit Assignment for Exploration in LLM Reasoningcited by 0
  8. 2026Knowing When Thinking Is Not Enough: Teaching Small Reasoning Models to Reason Beyond Their Parametric Knowledgecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026Improving reasoning at inference time via uncertainty minimisation
  2. 2026Understanding and Mitigating Premature Confidence for Better LLM Reasoning
  3. 2026EDIS: Diagnosing LLM Reasoning via Entropy Dynamics
  4. 2026Coherence Under Commitment: Probing Generalization and Vacuous Memorization in LLM Logical Reasoning
  5. 2026Statistical Early Stopping for Reasoning Models
  6. 2026The Stepwise Informativeness Assumption: Why are Entropy Dynamics and Reasoning Correlated in LLMs?
  7. 2025Revisiting the UID Hypothesis in LLM Reasoning Traces
  8. 2025Rethinking External Slow-Thinking: From Snowball Errors to Probability of Correct Reasoning